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cs.AI2026
Robust Reward Machine Learning and Execution in Noisy Environments
Roko Parac, Lorenzo Nodari, Daniel Furelos-Blanco +3
Background: Reward machines (RMs) represent non-Markovian reward functions as finite-state machines over high-level propositional events. Traditionally, these events are generated…
cs.AI2024★ 1 cited
Learning Robust Reward Machines from Noisy Labels
Roko Parac, Lorenzo Nodari, Leo Ardon +3
This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven R…